Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

Simple item page
dc.contributor.authorKontes, Georgios
dc.contributor.authorGiannakis, Georgios
dc.contributor.authorSánchez, Víctor
dc.contributor.authorde Agustin-Camacho, Pablo
dc.contributor.authorRomero-Amorrortu, Ander
dc.contributor.authorPanagiotidou, Natalia
dc.contributor.authorRovas, Dimitrios
dc.contributor.authorSteiger, Simone
dc.contributor.authorMutschler, Christopher
dc.contributor.authorGruen, Gunnar
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionEDIFICACIÓN DE ENERGÍA POSITIVA
dc.contributor.institutionLABORATORIO DE TRANSFORMACIÓN URBANA
dc.date.issued2018-12-01
dc.descriptionResearch leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project "Optimised Energy Efficient Design Platform for Refurbishment at District Level" under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information contained therein.
dc.description.abstractOver the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent2791671
dc.identifier.citationKontes , G , Giannakis , G , Sánchez , V , de Agustin-Camacho , P , Romero-Amorrortu , A , Panagiotidou , N , Rovas , D , Steiger , S , Mutschler , C & Gruen , G 2018 , ' Simulation-Based Evaluation and Optimization of Control Strategies in Buildings ' , Energies , vol. 11 , no. 12 , 3376; , pp. 3376 . https://doi.org/10.3390/en11123376
dc.identifier.doi10.3390/en11123376
dc.identifier.issn1996-1073
dc.identifier.otherresearchoutputwizard: 11556/663
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85059260922&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofEnergies
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsModel predictive control in buildings
dc.subject.keywordsReinforcement learning
dc.subject.keywordsData-driven control
dc.subject.keywordsSimulation model
dc.subject.keywordsMulti-criteria decision analysis
dc.subject.keywordsEnergyplus
dc.subject.keywordsModel predictive control in buildings
dc.subject.keywordsReinforcement learning
dc.subject.keywordsData-driven control
dc.subject.keywordsSimulation model
dc.subject.keywordsMulti-criteria decision analysis
dc.subject.keywordsEnergyplus
dc.subject.keywordsRenewable Energy, Sustainability and the Environment
dc.subject.keywordsFuel Technology
dc.subject.keywordsEngineering (miscellaneous)
dc.subject.keywordsEnergy Engineering and Power Technology
dc.subject.keywordsEnergy (miscellaneous)
dc.subject.keywordsControl and Optimization
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/680517/EU/Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability/MOEEBIUS
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/680676/EU/Optimised Energy Efficient Design Platform for Refurbishment at District Level/OptEEmAL
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/680517/EU/Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability/MOEEBIUS
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/680676/EU/Optimised Energy Efficient Design Platform for Refurbishment at District Level/OptEEmAL
dc.subject.keywordsFunding Info
dc.subject.keywordsResearch leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project "Optimised Energy Efficient Design Platform for Refurbishment at District Level" under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects on
dc.subject.keywordsResearch leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project "Optimised Energy Efficient Design Platform for Refurbishment at District Level" under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects on
dc.titleSimulation-Based Evaluation and Optimization of Control Strategies in Buildingsen
dc.typejournal article
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
energies-11-03376.pdf
Size:
2.66 MB
Format:
Adobe Portable Document Format
Download